Overview

Brought to you by YData

Dataset statistics

Number of variables30
Number of observations3783
Missing cells6647
Missing cells (%)5.9%
Duplicate rows124
Duplicate rows (%)3.3%
Total size in memory2.3 MiB
Average record size in memory623.9 B

Variable types

Categorical17
Text3
Numeric10

Alerts

study_room has constant value "0" Constant
servent_room has constant value "0" Constant
store_room has constant value "0" Constant
pooja_room has constant value "0" Constant
Dataset has 124 (3.3%) duplicate rowsDuplicates
Servant room is highly overall correlated with bathroom and 2 other fieldsHigh correlation
Store room is highly overall correlated with store roomHigh correlation
Study room is highly overall correlated with study roomHigh correlation
area is highly overall correlated with bathroom and 5 other fieldsHigh correlation
bathroom is highly overall correlated with Servant room and 6 other fieldsHigh correlation
bedRoom is highly overall correlated with area and 5 other fieldsHigh correlation
built_up_area is highly overall correlated with area and 4 other fieldsHigh correlation
carpet_area is highly overall correlated with area and 5 other fieldsHigh correlation
facing is highly overall correlated with built_up_areaHigh correlation
price is highly overall correlated with area and 7 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
property_type is highly overall correlated with bedRoom and 2 other fieldsHigh correlation
servant room is highly overall correlated with Servant room and 2 other fieldsHigh correlation
store room is highly overall correlated with Store roomHigh correlation
study room is highly overall correlated with Study roomHigh correlation
super_built_up_area is highly overall correlated with Servant room and 8 other fieldsHigh correlation
agePossession is highly imbalanced (78.2%) Imbalance
store room is highly imbalanced (56.4%) Imbalance
Store room is highly imbalanced (56.4%) Imbalance
facing has 845 (22.3%) missing values Missing
super_built_up_area has 1869 (49.4%) missing values Missing
built_up_area has 2068 (54.7%) missing values Missing
carpet_area has 1842 (48.7%) missing values Missing
area is highly skewed (γ1 = 30.22074765) Skewed
built_up_area is highly skewed (γ1 = 41.01297647) Skewed
carpet_area is highly skewed (γ1 = 24.77700984) Skewed
floorNum has 134 (3.5%) zeros Zeros
luxury_score has 634 (16.8%) zeros Zeros

Reproduction

Analysis started2025-05-06 01:33:02.265381
Analysis finished2025-05-06 01:33:32.176274
Duration29.91 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

property_type
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size196.8 KiB
flat
2941 
house
842 

Length

Max length5
Median length4
Mean length4.2225747
Min length4

Characters and Unicode

Total characters15974
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2941
77.7%
house 842
 
22.3%

Length

2025-05-06T07:03:32.372579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T07:03:32.602605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
flat 2941
77.7%
house 842
 
22.3%

Most occurring characters

ValueCountFrequency (%)
f 2941
18.4%
l 2941
18.4%
a 2941
18.4%
t 2941
18.4%
h 842
 
5.3%
o 842
 
5.3%
u 842
 
5.3%
s 842
 
5.3%
e 842
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15974
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2941
18.4%
l 2941
18.4%
a 2941
18.4%
t 2941
18.4%
h 842
 
5.3%
o 842
 
5.3%
u 842
 
5.3%
s 842
 
5.3%
e 842
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 15974
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2941
18.4%
l 2941
18.4%
a 2941
18.4%
t 2941
18.4%
h 842
 
5.3%
o 842
 
5.3%
u 842
 
5.3%
s 842
 
5.3%
e 842
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15974
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2941
18.4%
l 2941
18.4%
a 2941
18.4%
t 2941
18.4%
h 842
 
5.3%
o 842
 
5.3%
u 842
 
5.3%
s 842
 
5.3%
e 842
 
5.3%
Distinct674
Distinct (%)17.8%
Missing1
Missing (%)< 0.1%
Memory size243.7 KiB
2025-05-06T07:03:33.284890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.927816
Min length1

Characters and Unicode

Total characters64021
Distinct characters42
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique291 ?
Unique (%)7.7%

Sample

1st rowmaa bhagwati residency
2nd rowapna enclave
3rd rowtulsiani easy in homes
4th rowsmart world orchard
5th rowparkwood westend
ValueCountFrequency (%)
independent 486
 
4.9%
the 362
 
3.6%
dlf 224
 
2.2%
park 219
 
2.2%
city 169
 
1.7%
global 165
 
1.7%
signature 161
 
1.6%
m3m 156
 
1.6%
emaar 155
 
1.6%
heights 139
 
1.4%
Other values (783) 7745
77.6%
2025-05-06T07:03:34.350320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6898
 
10.8%
6201
 
9.7%
a 6063
 
9.5%
r 4333
 
6.8%
n 4244
 
6.6%
t 3829
 
6.0%
s 3616
 
5.6%
i 3465
 
5.4%
l 3056
 
4.8%
o 2856
 
4.5%
Other values (32) 19460
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 56769
88.7%
Space Separator 6201
 
9.7%
Decimal Number 549
 
0.9%
Uppercase Letter 484
 
0.8%
Other Punctuation 10
 
< 0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6898
12.2%
a 6063
 
10.7%
r 4333
 
7.6%
n 4244
 
7.5%
t 3829
 
6.7%
s 3616
 
6.4%
i 3465
 
6.1%
l 3056
 
5.4%
o 2856
 
5.0%
d 2535
 
4.5%
Other values (16) 15874
28.0%
Decimal Number
ValueCountFrequency (%)
3 215
39.2%
2 83
 
15.1%
1 76
 
13.8%
6 62
 
11.3%
8 35
 
6.4%
4 19
 
3.5%
5 17
 
3.1%
9 15
 
2.7%
7 14
 
2.6%
0 13
 
2.4%
Other Punctuation
ValueCountFrequency (%)
, 7
70.0%
/ 2
 
20.0%
. 1
 
10.0%
Space Separator
ValueCountFrequency (%)
6201
100.0%
Uppercase Letter
ValueCountFrequency (%)
I 484
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 57253
89.4%
Common 6768
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6898
12.0%
a 6063
 
10.6%
r 4333
 
7.6%
n 4244
 
7.4%
t 3829
 
6.7%
s 3616
 
6.3%
i 3465
 
6.1%
l 3056
 
5.3%
o 2856
 
5.0%
d 2535
 
4.4%
Other values (17) 16358
28.6%
Common
ValueCountFrequency (%)
6201
91.6%
3 215
 
3.2%
2 83
 
1.2%
1 76
 
1.1%
6 62
 
0.9%
8 35
 
0.5%
4 19
 
0.3%
5 17
 
0.3%
9 15
 
0.2%
7 14
 
0.2%
Other values (5) 31
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6898
 
10.8%
6201
 
9.7%
a 6063
 
9.5%
r 4333
 
6.8%
n 4244
 
6.6%
t 3829
 
6.0%
s 3616
 
5.6%
i 3465
 
5.4%
l 3056
 
4.8%
o 2856
 
4.5%
Other values (32) 19460
30.4%

sector
Text

Distinct123
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size215.8 KiB
2025-05-06T07:03:34.994971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length26
Median length9
Mean length9.3655829
Min length3

Characters and Unicode

Total characters35430
Distinct characters34
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 7
2nd rowsector 3
3rd rowsohna road
4th rowsector 61
5th rowsector 92
ValueCountFrequency (%)
sector 3507
46.2%
road 195
 
2.6%
sohna 175
 
2.3%
102 113
 
1.5%
85 109
 
1.4%
92 105
 
1.4%
69 94
 
1.2%
90 91
 
1.2%
65 90
 
1.2%
81 90
 
1.2%
Other values (121) 3028
39.9%
2025-05-06T07:03:35.993358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3900
11.0%
3830
10.8%
r 3813
10.8%
s 3788
10.7%
e 3639
10.3%
c 3564
10.1%
t 3531
10.0%
1 1071
 
3.0%
0 818
 
2.3%
a 805
 
2.3%
Other values (24) 6671
18.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24205
68.3%
Decimal Number 7395
 
20.9%
Space Separator 3830
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3900
16.1%
r 3813
15.8%
s 3788
15.6%
e 3639
15.0%
c 3564
14.7%
t 3531
14.6%
a 805
 
3.3%
d 283
 
1.2%
n 269
 
1.1%
h 234
 
1.0%
Other values (13) 379
 
1.6%
Decimal Number
ValueCountFrequency (%)
1 1071
14.5%
0 818
11.1%
9 796
10.8%
8 794
10.7%
6 751
10.2%
7 704
9.5%
3 691
9.3%
2 687
9.3%
5 597
8.1%
4 486
6.6%
Space Separator
ValueCountFrequency (%)
3830
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24205
68.3%
Common 11225
31.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3900
16.1%
r 3813
15.8%
s 3788
15.6%
e 3639
15.0%
c 3564
14.7%
t 3531
14.6%
a 805
 
3.3%
d 283
 
1.2%
n 269
 
1.1%
h 234
 
1.0%
Other values (13) 379
 
1.6%
Common
ValueCountFrequency (%)
3830
34.1%
1 1071
 
9.5%
0 818
 
7.3%
9 796
 
7.1%
8 794
 
7.1%
6 751
 
6.7%
7 704
 
6.3%
3 691
 
6.2%
2 687
 
6.1%
5 597
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 3900
11.0%
3830
10.8%
r 3813
10.8%
s 3788
10.7%
e 3639
10.3%
c 3564
10.1%
t 3531
10.0%
1 1071
 
3.0%
0 818
 
2.3%
a 805
 
2.3%
Other values (24) 6671
18.8%

price
Real number (ℝ)

High correlation 

Distinct473
Distinct (%)12.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.5055579
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.7 KiB
2025-05-06T07:03:36.293354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.94
median1.5
Q32.7
95-th percentile8.49
Maximum31.5
Range31.43
Interquartile range (IQR)1.76

Descriptive statistics

Standard deviation2.9504779
Coefficient of variation (CV)1.1775732
Kurtosis15.263879
Mean2.5055579
Median Absolute Deviation (MAD)0.71
Skewness3.3125368
Sum9476.02
Variance8.7053197
MonotonicityNot monotonic
2025-05-06T07:03:36.624639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 83
 
2.2%
0.9 68
 
1.8%
1.1 66
 
1.7%
1.2 66
 
1.7%
1.5 66
 
1.7%
1.4 62
 
1.6%
1.3 60
 
1.6%
0.95 58
 
1.5%
2 56
 
1.5%
1 51
 
1.3%
Other values (463) 3146
83.2%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 9
0.2%
0.21 6
0.2%
0.22 9
0.2%
0.23 1
 
< 0.1%
0.24 7
0.2%
0.25 11
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

High correlation 

Distinct2650
Distinct (%)70.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean13798.52
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.7 KiB
2025-05-06T07:03:36.963218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4717.3
Q16808.75
median9000
Q313763.25
95-th percentile33208.55
Maximum600000
Range599996
Interquartile range (IQR)6954.5

Descriptive statistics

Standard deviation23058.016
Coefficient of variation (CV)1.67105
Kurtosis186.99776
Mean13798.52
Median Absolute Deviation (MAD)2757.5
Skewness11.439386
Sum52186001
Variance5.3167208 × 108
MonotonicityNot monotonic
2025-05-06T07:03:37.299612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 28
 
0.7%
8000 19
 
0.5%
5000 17
 
0.4%
12500 17
 
0.4%
6666 14
 
0.4%
7500 14
 
0.4%
11111 14
 
0.4%
8333 13
 
0.3%
22222 13
 
0.3%
6000 11
 
0.3%
Other values (2640) 3622
95.7%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

High correlation  Skewed 

Distinct1968
Distinct (%)52.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2846.899
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.7 KiB
2025-05-06T07:03:37.632125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile519.1
Q11221.075
median1725.15
Q32295.075
95-th percentile4200.1
Maximum875000
Range874950
Interquartile range (IQR)1074

Descriptive statistics

Standard deviation22792.358
Coefficient of variation (CV)8.0060298
Kurtosis973.41815
Mean2846.899
Median Absolute Deviation (MAD)525.15
Skewness30.220748
Sum10766972
Variance5.194916 × 108
MonotonicityNot monotonic
2025-05-06T07:03:37.935310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3240 43
 
1.1%
2700 37
 
1.0%
2000 35
 
0.9%
1350 34
 
0.9%
1800 34
 
0.9%
900 30
 
0.8%
1950.1 22
 
0.6%
2250 22
 
0.6%
1650 21
 
0.6%
1000 19
 
0.5%
Other values (1958) 3485
92.1%
ValueCountFrequency (%)
50 4
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
0.1%
61 1
 
< 0.1%
67 2
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
76 1
 
< 0.1%
ValueCountFrequency (%)
875000 1
< 0.1%
642857.1 1
< 0.1%
620000 1
< 0.1%
566666.7 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517.2 2
0.1%
65261 1
< 0.1%
58227.8 1
< 0.1%
Distinct2350
Distinct (%)62.1%
Missing0
Missing (%)0.0%
Memory size380.4 KiB
2025-05-06T07:03:38.763121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length124
Median length119
Mean length53.920962
Min length12

Characters and Unicode

Total characters203983
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1779 ?
Unique (%)47.0%

Sample

1st rowCarpet area: 900 (83.61 sq.m.)
2nd rowCarpet area: 650 (60.39 sq.m.)
3rd rowCarpet area: 595 (55.28 sq.m.)
4th rowCarpet area: 1200 (111.48 sq.m.)
5th rowSuper Built up area 1345(124.95 sq.m.)
ValueCountFrequency (%)
area 5703
18.5%
sq.m 3759
12.2%
up 3096
 
10.0%
built 2390
 
7.7%
super 1914
 
6.2%
sq.ft 1779
 
5.8%
sq.m.)carpet 1206
 
3.9%
carpet 731
 
2.4%
sq.m.)built 704
 
2.3%
plot 666
 
2.2%
Other values (2842) 8927
28.9%
2025-05-06T07:03:39.975764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27092
 
13.3%
. 20827
 
10.2%
a 13478
 
6.6%
r 9689
 
4.7%
e 9558
 
4.7%
1 9446
 
4.6%
s 7712
 
3.8%
q 7581
 
3.7%
t 7482
 
3.7%
p 6951
 
3.4%
Other values (25) 84167
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 84611
41.5%
Decimal Number 48209
23.6%
Space Separator 27092
 
13.3%
Other Punctuation 23950
 
11.7%
Uppercase Letter 8799
 
4.3%
Close Punctuation 5661
 
2.8%
Open Punctuation 5661
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13478
15.9%
r 9689
11.5%
e 9558
11.3%
s 7712
9.1%
q 7581
9.0%
t 7482
8.8%
p 6951
8.2%
u 6924
8.2%
m 5671
6.7%
l 3762
 
4.4%
Other values (5) 5803
6.9%
Decimal Number
ValueCountFrequency (%)
1 9446
19.6%
0 6744
14.0%
2 5817
12.1%
5 4834
10.0%
3 4053
8.4%
4 3783
7.8%
6 3757
 
7.8%
7 3328
 
6.9%
8 3232
 
6.7%
9 3215
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 3096
35.2%
C 1941
22.1%
S 1914
21.8%
U 1182
 
13.4%
P 666
 
7.6%
Other Punctuation
ValueCountFrequency (%)
. 20827
87.0%
: 3123
 
13.0%
Space Separator
ValueCountFrequency (%)
27092
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5661
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5661
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 110573
54.2%
Latin 93410
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13478
14.4%
r 9689
10.4%
e 9558
10.2%
s 7712
8.3%
q 7581
8.1%
t 7482
8.0%
p 6951
7.4%
u 6924
7.4%
m 5671
 
6.1%
l 3762
 
4.0%
Other values (10) 14602
15.6%
Common
ValueCountFrequency (%)
27092
24.5%
. 20827
18.8%
1 9446
 
8.5%
0 6744
 
6.1%
2 5817
 
5.3%
) 5661
 
5.1%
( 5661
 
5.1%
5 4834
 
4.4%
3 4053
 
3.7%
4 3783
 
3.4%
Other values (5) 16655
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27092
 
13.3%
. 20827
 
10.2%
a 13478
 
6.6%
r 9689
 
4.7%
e 9558
 
4.7%
1 9446
 
4.6%
s 7712
 
3.8%
q 7581
 
3.7%
t 7482
 
3.7%
p 6951
 
3.4%
Other values (25) 84167
41.3%

bedRoom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3251388
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.7 KiB
2025-05-06T07:03:40.296596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8594222
Coefficient of variation (CV)0.55920138
Kurtosis18.923485
Mean3.3251388
Median Absolute Deviation (MAD)1
Skewness3.5298549
Sum12579
Variance3.4574509
MonotonicityNot monotonic
2025-05-06T07:03:40.606594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1545
40.8%
2 991
26.2%
4 675
17.8%
5 200
 
5.3%
1 130
 
3.4%
6 74
 
2.0%
9 40
 
1.1%
8 30
 
0.8%
7 28
 
0.7%
12 27
 
0.7%
Other values (9) 43
 
1.1%
ValueCountFrequency (%)
1 130
 
3.4%
2 991
26.2%
3 1545
40.8%
4 675
17.8%
5 200
 
5.3%
6 74
 
2.0%
7 28
 
0.7%
8 30
 
0.8%
9 40
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 11
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 27
0.7%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3925456
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.7 KiB
2025-05-06T07:03:40.896595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9108965
Coefficient of variation (CV)0.56326333
Kurtosis18.157246
Mean3.3925456
Median Absolute Deviation (MAD)1
Skewness3.2786636
Sum12834
Variance3.6515256
MonotonicityNot monotonic
2025-05-06T07:03:41.189127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1111
29.4%
2 1103
29.2%
4 836
22.1%
5 293
 
7.7%
1 159
 
4.2%
6 120
 
3.2%
9 40
 
1.1%
7 39
 
1.0%
8 24
 
0.6%
12 21
 
0.6%
Other values (9) 37
 
1.0%
ValueCountFrequency (%)
1 159
 
4.2%
2 1103
29.2%
3 1111
29.4%
4 836
22.1%
5 293
 
7.7%
6 120
 
3.2%
7 39
 
1.0%
8 24
 
0.6%
9 40
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 7
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 21
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
3
1445 
2
921 
3+
853 
1
375 
0
189 

Length

Max length2
Median length1
Mean length1.2254824
Min length1

Characters and Unicode

Total characters4636
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3 1445
38.2%
2 921
24.3%
3+ 853
22.5%
1 375
 
9.9%
0 189
 
5.0%

Length

2025-05-06T07:03:41.545984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T07:03:41.802103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2298
60.7%
2 921
24.3%
1 375
 
9.9%
0 189
 
5.0%

Most occurring characters

ValueCountFrequency (%)
3 2298
49.6%
2 921
19.9%
+ 853
 
18.4%
1 375
 
8.1%
0 189
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3783
81.6%
Math Symbol 853
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2298
60.7%
2 921
24.3%
1 375
 
9.9%
0 189
 
5.0%
Math Symbol
ValueCountFrequency (%)
+ 853
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4636
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2298
49.6%
2 921
19.9%
+ 853
 
18.4%
1 375
 
8.1%
0 189
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4636
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2298
49.6%
2 921
19.9%
+ 853
 
18.4%
1 375
 
8.1%
0 189
 
4.1%

floorNum
Real number (ℝ)

Zeros 

Distinct43
Distinct (%)1.1%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.8312965
Minimum0
Maximum51
Zeros134
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size29.7 KiB
2025-05-06T07:03:42.105948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0359864
Coefficient of variation (CV)0.88357845
Kurtosis4.5220051
Mean6.8312965
Median Absolute Deviation (MAD)3
Skewness1.6920416
Sum25713
Variance36.433131
MonotonicityNot monotonic
2025-05-06T07:03:42.502744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 508
13.4%
2 499
13.2%
1 363
 
9.6%
4 323
 
8.5%
8 197
 
5.2%
6 186
 
4.9%
10 186
 
4.9%
7 183
 
4.8%
5 177
 
4.7%
9 170
 
4.5%
Other values (33) 972
25.7%
ValueCountFrequency (%)
0 134
 
3.5%
1 363
9.6%
2 499
13.2%
3 508
13.4%
4 323
8.5%
5 177
 
4.7%
6 186
 
4.9%
7 183
 
4.8%
8 197
 
5.2%
9 170
 
4.5%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 2
0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

High correlation  Missing 

Distinct9
Distinct (%)0.3%
Missing845
Missing (%)22.3%
Memory size207.8 KiB
East
639 
North-East
637 
North
397 
West
252 
NotAvailable
252 
Other values (4)
761 

Length

Max length12
Median length10
Mean length7.2797822
Min length4

Characters and Unicode

Total characters21388
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWest
2nd rowWest
3rd rowNorth-East
4th rowSouth-East
5th rowEast

Common Values

ValueCountFrequency (%)
East 639
16.9%
North-East 637
16.8%
North 397
10.5%
West 252
 
6.7%
NotAvailable 252
 
6.7%
South 233
 
6.2%
North-West 199
 
5.3%
South-East 172
 
4.5%
South-West 157
 
4.2%
(Missing) 845
22.3%

Length

2025-05-06T07:03:42.832900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T07:03:43.201648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
east 639
21.7%
north-east 637
21.7%
north 397
13.5%
west 252
 
8.6%
notavailable 252
 
8.6%
south 233
 
7.9%
north-west 199
 
6.8%
south-east 172
 
5.9%
south-west 157
 
5.3%

Most occurring characters

ValueCountFrequency (%)
t 4103
19.2%
s 2056
9.6%
o 2047
9.6%
a 1952
9.1%
h 1795
8.4%
N 1485
 
6.9%
E 1448
 
6.8%
r 1233
 
5.8%
- 1165
 
5.4%
e 860
 
4.0%
Other values (8) 3244
15.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15868
74.2%
Uppercase Letter 4355
 
20.4%
Dash Punctuation 1165
 
5.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 4103
25.9%
s 2056
13.0%
o 2047
12.9%
a 1952
12.3%
h 1795
11.3%
r 1233
 
7.8%
e 860
 
5.4%
u 562
 
3.5%
l 504
 
3.2%
v 252
 
1.6%
Other values (2) 504
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
N 1485
34.1%
E 1448
33.2%
W 608
14.0%
S 562
 
12.9%
A 252
 
5.8%
Dash Punctuation
ValueCountFrequency (%)
- 1165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20223
94.6%
Common 1165
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 4103
20.3%
s 2056
10.2%
o 2047
10.1%
a 1952
9.7%
h 1795
8.9%
N 1485
 
7.3%
E 1448
 
7.2%
r 1233
 
6.1%
e 860
 
4.3%
W 608
 
3.0%
Other values (7) 2636
13.0%
Common
ValueCountFrequency (%)
- 1165
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21388
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 4103
19.2%
s 2056
9.6%
o 2047
9.6%
a 1952
9.1%
h 1795
8.4%
N 1485
 
6.9%
E 1448
 
6.8%
r 1233
 
5.8%
- 1165
 
5.4%
e 860
 
4.0%
Other values (8) 3244
15.2%

agePossession
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size215.6 KiB
Undefined
3651 
Under Construction
 
132

Length

Max length18
Median length9
Mean length9.3140365
Min length9

Characters and Unicode

Total characters35235
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUndefined
2nd rowUndefined
3rd rowUndefined
4th rowUndefined
5th rowUnder Construction

Common Values

ValueCountFrequency (%)
Undefined 3651
96.5%
Under Construction 132
 
3.5%

Length

2025-05-06T07:03:43.542589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T07:03:43.775462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
undefined 3651
93.3%
under 132
 
3.4%
construction 132
 
3.4%

Most occurring characters

ValueCountFrequency (%)
n 7698
21.8%
d 7434
21.1%
e 7434
21.1%
U 3783
10.7%
i 3783
10.7%
f 3651
10.4%
r 264
 
0.7%
o 264
 
0.7%
t 264
 
0.7%
132
 
0.4%
Other values (4) 528
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31188
88.5%
Uppercase Letter 3915
 
11.1%
Space Separator 132
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 7698
24.7%
d 7434
23.8%
e 7434
23.8%
i 3783
12.1%
f 3651
11.7%
r 264
 
0.8%
o 264
 
0.8%
t 264
 
0.8%
s 132
 
0.4%
u 132
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
U 3783
96.6%
C 132
 
3.4%
Space Separator
ValueCountFrequency (%)
132
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 35103
99.6%
Common 132
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 7698
21.9%
d 7434
21.2%
e 7434
21.2%
U 3783
10.8%
i 3783
10.8%
f 3651
10.4%
r 264
 
0.8%
o 264
 
0.8%
t 264
 
0.8%
C 132
 
0.4%
Other values (3) 396
 
1.1%
Common
ValueCountFrequency (%)
132
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35235
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 7698
21.8%
d 7434
21.1%
e 7434
21.1%
U 3783
10.7%
i 3783
10.7%
f 3651
10.4%
r 264
 
0.7%
o 264
 
0.7%
t 264
 
0.7%
132
 
0.4%
Other values (4) 528
 
1.5%

super_built_up_area
Real number (ℝ)

High correlation  Missing 

Distinct593
Distinct (%)31.0%
Missing1869
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean1921.4083
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.7 KiB
2025-05-06T07:03:44.085230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile761.05
Q11457
median1827.75
Q32215
95-th percentile3187.45
Maximum10000
Range9911
Interquartile range (IQR)758

Descriptive statistics

Standard deviation767.28268
Coefficient of variation (CV)0.39933348
Kurtosis10.083867
Mean1921.4083
Median Absolute Deviation (MAD)372.25
Skewness1.8241603
Sum3677575.5
Variance588722.7
MonotonicityNot monotonic
2025-05-06T07:03:44.405084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 38
 
1.0%
1650 38
 
1.0%
2000 26
 
0.7%
1578 25
 
0.7%
2150 23
 
0.6%
1640 22
 
0.6%
2408 20
 
0.5%
1900 19
 
0.5%
1350 19
 
0.5%
1930 18
 
0.5%
Other values (583) 1666
44.0%
(Missing) 1869
49.4%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 2
0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct641
Distinct (%)37.4%
Missing2068
Missing (%)54.7%
Infinite0
Infinite (%)0.0%
Mean2367.9157
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.7 KiB
2025-05-06T07:03:44.753919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile247.3
Q11112
median1650
Q32399.5
95-th percentile4631.5
Maximum737147
Range737145
Interquartile range (IQR)1287.5

Descriptive statistics

Standard deviation17810.819
Coefficient of variation (CV)7.5217287
Kurtosis1692.8981
Mean2367.9157
Median Absolute Deviation (MAD)600
Skewness41.012976
Sum4060975.4
Variance3.1722528 × 108
MonotonicityNot monotonic
2025-05-06T07:03:45.412929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 40
 
1.1%
3240 37
 
1.0%
1350 34
 
0.9%
1900 34
 
0.9%
2700 32
 
0.8%
900 28
 
0.7%
1600 26
 
0.7%
2000 25
 
0.7%
1300 25
 
0.7%
1700 23
 
0.6%
Other values (631) 1411
37.3%
(Missing) 2068
54.7%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
50 3
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 5
0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8067.8 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
 
0.1%

carpet_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct732
Distinct (%)37.7%
Missing1842
Missing (%)48.7%
Infinite0
Infinite (%)0.0%
Mean2486.3909
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.7 KiB
2025-05-06T07:03:45.750982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1824
median1295
Q31790
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)966

Descriptive statistics

Standard deviation22392.404
Coefficient of variation (CV)9.005987
Kurtosis626.86987
Mean2486.3909
Median Absolute Deviation (MAD)473
Skewness24.77701
Sum4826084.7
Variance5.0141976 × 108
MonotonicityNot monotonic
2025-05-06T07:03:46.172354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1800 36
 
1.0%
1600 36
 
1.0%
1200 32
 
0.8%
1500 30
 
0.8%
1650 28
 
0.7%
1350 28
 
0.7%
1450 23
 
0.6%
1300 23
 
0.6%
1000 22
 
0.6%
Other values (722) 1641
43.4%
(Missing) 1842
48.7%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 2
0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study_room
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size184.8 KiB
0
3783 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3783
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3783
100.0%

Length

2025-05-06T07:03:46.551125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T07:03:46.776126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3783
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3783
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3783
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3783
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3783
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3783
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3783
100.0%

servent_room
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size184.8 KiB
0
3783 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3783
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3783
100.0%

Length

2025-05-06T07:03:46.993347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T07:03:47.266627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3783
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3783
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3783
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3783
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3783
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3783
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3783
100.0%

store_room
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size184.8 KiB
0
3783 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3783
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3783
100.0%

Length

2025-05-06T07:03:47.516650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T07:03:47.775343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3783
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3783
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3783
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3783
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3783
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3783
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3783
100.0%

pooja_room
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size184.8 KiB
0
3783 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3783
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3783
100.0%

Length

2025-05-06T07:03:47.990365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T07:03:48.250553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3783
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3783
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3783
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3783
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3783
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3783
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3783
100.0%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size184.8 KiB
0
3365 
1
418 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3783
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3365
89.0%
1 418
 
11.0%

Length

2025-05-06T07:03:48.487618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T07:03:48.798677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3365
89.0%
1 418
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 3365
89.0%
1 418
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3783
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3365
89.0%
1 418
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3783
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3365
89.0%
1 418
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3365
89.0%
1 418
 
11.0%

study room
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size184.8 KiB
0
3070 
1
713 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3783
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 3070
81.2%
1 713
 
18.8%

Length

2025-05-06T07:03:49.043677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T07:03:49.317555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3070
81.2%
1 713
 
18.8%

Most occurring characters

ValueCountFrequency (%)
0 3070
81.2%
1 713
 
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3783
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3070
81.2%
1 713
 
18.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3783
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3070
81.2%
1 713
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3070
81.2%
1 713
 
18.8%

servant room
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size184.8 KiB
0
2437 
1
1346 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3783
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2437
64.4%
1 1346
35.6%

Length

2025-05-06T07:03:49.592555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T07:03:49.842546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2437
64.4%
1 1346
35.6%

Most occurring characters

ValueCountFrequency (%)
0 2437
64.4%
1 1346
35.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3783
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2437
64.4%
1 1346
35.6%

Most occurring scripts

ValueCountFrequency (%)
Common 3783
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2437
64.4%
1 1346
35.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2437
64.4%
1 1346
35.6%

store room
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size184.8 KiB
0
3443 
1
 
340

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3783
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3443
91.0%
1 340
 
9.0%

Length

2025-05-06T07:03:50.093469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T07:03:50.365317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3443
91.0%
1 340
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 3443
91.0%
1 340
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3783
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3443
91.0%
1 340
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3783
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3443
91.0%
1 340
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3443
91.0%
1 340
 
9.0%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size184.8 KiB
0
3127 
1
656 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3783
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3127
82.7%
1 656
 
17.3%

Length

2025-05-06T07:03:50.647105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T07:03:50.882102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3127
82.7%
1 656
 
17.3%

Most occurring characters

ValueCountFrequency (%)
0 3127
82.7%
1 656
 
17.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3783
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3127
82.7%
1 656
 
17.3%

Most occurring scripts

ValueCountFrequency (%)
Common 3783
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3127
82.7%
1 656
 
17.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3127
82.7%
1 656
 
17.3%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size184.8 KiB
0
2522 
2
1051 
1
 
210

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3783
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2522
66.7%
2 1051
27.8%
1 210
 
5.6%

Length

2025-05-06T07:03:51.162102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T07:03:51.402107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2522
66.7%
2 1051
27.8%
1 210
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 2522
66.7%
2 1051
27.8%
1 210
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3783
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2522
66.7%
2 1051
27.8%
1 210
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 3783
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2522
66.7%
2 1051
27.8%
1 210
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2522
66.7%
2 1051
27.8%
1 210
 
5.6%

Study room
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size184.8 KiB
0
3070 
1
713 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3783
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 3070
81.2%
1 713
 
18.8%

Length

2025-05-06T07:03:51.717103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T07:03:51.987104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3070
81.2%
1 713
 
18.8%

Most occurring characters

ValueCountFrequency (%)
0 3070
81.2%
1 713
 
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3783
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3070
81.2%
1 713
 
18.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3783
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3070
81.2%
1 713
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3070
81.2%
1 713
 
18.8%

Servant room
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size184.8 KiB
0
2437 
1
1346 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3783
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2437
64.4%
1 1346
35.6%

Length

2025-05-06T07:03:52.286244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T07:03:52.523351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2437
64.4%
1 1346
35.6%

Most occurring characters

ValueCountFrequency (%)
0 2437
64.4%
1 1346
35.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3783
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2437
64.4%
1 1346
35.6%

Most occurring scripts

ValueCountFrequency (%)
Common 3783
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2437
64.4%
1 1346
35.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2437
64.4%
1 1346
35.6%

Store room
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size184.8 KiB
0
3443 
1
 
340

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3783
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3443
91.0%
1 340
 
9.0%

Length

2025-05-06T07:03:52.853640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T07:03:53.132440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3443
91.0%
1 340
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 3443
91.0%
1 340
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3783
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3443
91.0%
1 340
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3783
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3443
91.0%
1 340
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3443
91.0%
1 340
 
9.0%

luxury_score
Real number (ℝ)

Zeros 

Distinct160
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.715834
Minimum0
Maximum174
Zeros634
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size29.7 KiB
2025-05-06T07:03:53.408088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q124
median53
Q3109
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)85

Descriptive statistics

Standard deviation54.567235
Coefficient of variation (CV)0.79409988
Kurtosis-0.93364054
Mean68.715834
Median Absolute Deviation (MAD)43
Skewness0.46516108
Sum259952
Variance2977.5832
MonotonicityNot monotonic
2025-05-06T07:03:53.795560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 634
 
16.8%
49 348
 
9.2%
174 196
 
5.2%
44 59
 
1.6%
38 58
 
1.5%
72 56
 
1.5%
165 55
 
1.5%
37 49
 
1.3%
42 46
 
1.2%
7 43
 
1.1%
Other values (150) 2239
59.2%
ValueCountFrequency (%)
0 634
16.8%
5 6
 
0.2%
6 6
 
0.2%
7 43
 
1.1%
8 30
 
0.8%
9 9
 
0.2%
12 7
 
0.2%
13 10
 
0.3%
14 12
 
0.3%
15 42
 
1.1%
ValueCountFrequency (%)
174 196
5.2%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 11
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 27
 
0.7%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2025-05-06T07:03:27.596109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:05.644169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:07.920337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:10.653959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:13.060183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:15.784765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:18.240071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:20.516076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:22.858238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:25.161449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:27.816106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:05.852145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:08.179362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:10.918969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:13.300320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:16.031122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:18.486240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:20.746057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:23.086800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:25.371178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:28.042941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:06.088276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:08.424363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:11.148961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:13.574868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:16.301754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:18.708332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:20.976053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:23.319812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:25.705407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:28.570391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:06.303383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:08.644366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:11.363969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:13.789869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:16.544310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:18.920701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:21.222430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:23.554358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:25.923858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:28.800057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:06.549381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:08.894367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:11.615768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:14.034868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:16.789942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:19.145527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:21.532326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:23.804358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:26.204048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:29.029462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:06.814374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:09.458500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:11.846107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:14.334931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:17.024947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:19.410510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:21.749728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:24.069356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:26.444058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:29.269456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:07.014371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:09.715510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:12.064999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:14.553724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:17.254939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:19.615506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:21.964734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:24.279822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:26.664053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:29.479452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:07.214372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:09.946807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:12.299094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:14.765189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:17.505670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:19.820528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:22.177696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:24.504823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:26.899960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:29.715266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:07.465208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:10.177860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:12.569686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:15.188102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:17.769016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:20.046060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:22.362674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:24.729821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:27.114965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:29.935266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:07.694338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:10.422855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:12.794683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:15.514894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:18.004012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:20.266053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:22.607675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:24.931441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-06T07:03:27.346109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-05-06T07:03:54.113194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Servant roomStore roomStudy roomagePossessionareabalconybathroombedRoombuilt_up_areacarpet_areafacingfloorNumfurnishing_typeluxury_scoreotherspooja roompriceprice_per_sqftproperty_typeservant roomstore roomstudy roomsuper_built_up_area
Servant room1.0000.1590.1780.0930.0150.4160.5180.3160.0000.0000.1740.0820.2670.3660.0000.2480.3680.0410.0660.9990.1590.1780.588
Store room0.1591.0000.2230.0440.0390.1650.2410.2230.0000.0000.0440.1070.1590.2350.1020.3060.3000.0000.2410.1590.9980.2230.043
Study room0.1780.2231.0000.0000.0180.2010.1700.1500.0000.0050.0670.0760.1350.1960.0270.3100.2420.0290.1240.1780.2230.9990.116
agePossession0.0930.0440.0001.0000.0000.0000.0800.0820.0000.0000.0420.0840.1200.1030.0080.0760.0440.0000.0850.0930.0440.0000.088
area0.0150.0390.0180.0001.0000.0060.6920.6310.8370.8060.0230.1160.0430.2570.0410.0380.7450.2050.0290.0150.0390.0180.949
balcony0.4160.1650.2010.0000.0061.0000.2010.1550.0000.0250.2270.1250.1510.2380.0780.1450.1370.0430.3380.4160.1650.2010.304
bathroom0.5180.2410.1700.0800.6920.2011.0000.8630.4910.6110.035-0.0030.1910.1920.0640.2820.7210.4060.4680.5180.2410.1700.822
bedRoom0.3160.2230.1500.0820.6310.1550.8631.0000.4040.5820.070-0.0960.1640.0790.0700.2890.6830.4110.5910.3160.2230.1500.802
built_up_area0.0000.0000.0000.0000.8370.0000.4910.4041.0000.9671.0000.0860.0900.2810.0000.0000.6040.1290.0000.0000.0000.0000.927
carpet_area0.0000.0000.0050.0000.8060.0250.6110.5820.9671.0000.0000.1500.0000.2420.0170.0000.6220.1420.0000.0000.0000.0050.895
facing0.1740.0440.0670.0420.0230.2270.0350.0701.0000.0001.0000.0870.1160.1950.0370.0700.0280.0500.4890.1740.0440.0670.000
floorNum0.0820.1070.0760.0840.1160.125-0.003-0.0960.0860.1500.0871.0000.0290.2130.0290.0980.004-0.1200.4710.0820.1070.0760.156
furnishing_type0.2670.1590.1350.1200.0430.1510.1910.1640.0900.0000.1160.0291.0000.2480.0550.2130.1740.0200.0870.2670.1590.1350.133
luxury_score0.3660.2350.1960.1030.2570.2380.1920.0790.2810.2420.1950.2130.2481.0000.1800.2060.2110.0550.2780.3660.2350.1960.234
others0.0000.1020.0270.0080.0410.0780.0640.0700.0000.0170.0370.0290.0550.1801.0000.0290.0330.0350.0220.0000.1020.0270.082
pooja room0.2480.3060.3100.0760.0380.1450.2820.2890.0000.0000.0700.0980.2130.2060.0291.0000.3340.0440.2520.2480.3060.3100.154
price0.3680.3000.2420.0440.7450.1370.7210.6830.6040.6220.0280.0040.1740.2110.0330.3341.0000.7430.5410.3680.3000.2420.774
price_per_sqft0.0410.0000.0290.0000.2050.0430.4060.4110.1290.1420.050-0.1200.0200.0550.0350.0440.7431.0000.1990.0410.0000.0290.286
property_type0.0660.2410.1240.0850.0290.3380.4680.5910.0000.0000.4890.4710.0870.2780.0220.2520.5410.1991.0000.0660.2410.1241.000
servant room0.9990.1590.1780.0930.0150.4160.5180.3160.0000.0000.1740.0820.2670.3660.0000.2480.3680.0410.0661.0000.1590.1780.588
store room0.1590.9980.2230.0440.0390.1650.2410.2230.0000.0000.0440.1070.1590.2350.1020.3060.3000.0000.2410.1591.0000.2230.043
study room0.1780.2230.9990.0000.0180.2010.1700.1500.0000.0050.0670.0760.1350.1960.0270.3100.2420.0290.1240.1780.2231.0000.116
super_built_up_area0.5880.0430.1160.0880.9490.3040.8220.8020.9270.8950.0000.1560.1330.2340.0820.1540.7740.2861.0000.5880.0430.1161.000

Missing values

2025-05-06T07:03:30.351719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-06T07:03:31.334162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-06T07:03:31.904171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy_roomservent_roomstore_roompooja_roomothersstudy roomservant roomstore roompooja roomfurnishing_typeStudy roomServant roomStore roomluxury_score
0flatmaa bhagwati residencysector 70.455000.0900.0Carpet area: 900 (83.61 sq.m.)2214.0WestUndefinedNaNNaN900.0000000000000028
1flatapna enclavesector 30.507692.0650.0Carpet area: 650 (60.39 sq.m.)2211.0WestUndefinedNaNNaN650.0000000000200037
2flattulsiani easy in homessohna road0.406722.0595.1Carpet area: 595 (55.28 sq.m.)22312.0NaNUndefinedNaNNaN595.0000000000000036
3flatsmart world orchardsector 611.4712250.01200.0Carpet area: 1200 (111.48 sq.m.)2222.0NaNUndefinedNaNNaN1200.0000001000010076
4flatparkwood westendsector 920.705204.01345.1Super Built up area 1345(124.95 sq.m.)2235.0NaNUnder Construction1345.0NaNNaN00000100001000
5flatsignature global infinity mallsector 360.416269.0654.0Built Up area: 654 (60.76 sq.m.)2233.0NaNUndefinedNaN654.0NaN00000000000000
6flatthe cocoondwarka expressway2.0013333.01500.0Super Built up area 1500(139.35 sq.m.)3335.0NaNUndefined1500.0NaNNaN00000000000000
7flatats triumphsector 1041.807860.02290.1Carpet area: 2290 (212.75 sq.m.)34314.0NaNUndefinedNaNNaN2290.0000000000000060
8flatvatika xpressionssector 88b1.108148.01350.0Built Up area: 1350 (125.42 sq.m.)Carpet area: 1050 sq.ft. (97.55 sq.m.)243+2.0North-EastUnder ConstructionNaN1350.01050.0000001000010058
9flatraheja revantasector 784.7516885.02813.1Built Up area: 2813 (261.34 sq.m.)33231.0NaNUndefinedNaN2813.0NaN0000001000010100
property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy_roomservent_roomstore_roompooja_roomothersstudy roomservant roomstore roompooja roomfurnishing_typeStudy roomServant roomStore roomluxury_score
3773houseIndependentsector 313.5024155.01449.0Plot area 161(134.62 sq.m.)4332.0South-WestUndefinedNaN1449.0NaN000000010200181
3774houseIndependentsector 465.6523870.02367.0Plot area 263(219.9 sq.m.)8633.0South-WestUndefinedNaN2367.0NaN000000100201067
3775houseIndependentsector 463.5524500.01449.0Plot area 161(134.62 sq.m.)5433.0North-WestUndefinedNaN1449.0NaN000000100201073
3776houseIndependentsector 463.6024845.01449.0Plot area 161(134.62 sq.m.)5533.0South-EastUndefinedNaN1449.0NaN000000100201075
3777houseIndependentsector 553.1020026.01548.0Plot area 172(143.81 sq.m.)5432.0North-EastUndefinedNaN1548.0NaN000000110201159
3778houseIndependentsector 574.7528787.01650.0Plot area 1600(148.64 sq.m.)Built Up area: 1700 sq.ft. (157.94 sq.m.)Carpet area: 1650 sq.ft. (153.29 sq.m.)3332.0North-WestUndefinedNaN1700.01650.0000000010200196
3779housedlf city phase 1sector 265.5030556.01800.0Plot area 200(167.23 sq.m.)4432.0North-EastUndefinedNaN1800.0NaN000001101011069
3780housedlf city plots phase 2sector 254.2531481.01350.0Plot area 150(125.42 sq.m.)3232.0NorthUndefinedNaN1350.0NaN000001000010035
3781housedlf city phase 1sector 264.5033333.01350.0Plot area 150(125.42 sq.m.)3322.0EastUndefinedNaN1350.0NaN000001100011070
3782housedlf city phase 1sector 263.2533129.0981.0Plot area 109(91.14 sq.m.)3332.0WestUndefinedNaN981.0NaN000001000010079

Duplicate rows

Most frequently occurring

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy_roomservent_roomstore_roompooja_roomothersstudy roomservant roomstore roompooja roomfurnishing_typeStudy roomServant roomStore roomluxury_score# duplicates
0flatambience caitrionasector 2414.00200000.0700.0Built Up area: 700 (65.03 sq.m.)4533.0EastUndefinedNaN700.0NaN000000000000002
1flatansal heights 86sector 860.905325.01690.1Built Up area: 1690 (157.01 sq.m.)33210.0NaNUndefinedNaN1690.0NaN0000000000000292
2flatansal heights 86sector 861.304666.02786.1Super Built up area 2786(258.83 sq.m.)46211.0EastUndefined2786.0NaNNaN0000101000010862
3flatansal housing highland parksector 1030.886429.01368.8Super Built up area 1361(126.44 sq.m.)2233.0NaNUndefined1361.0NaNNaN0000000000000522
4flatantriksh heightssector 840.855556.01529.9Super Built up area 1350(125.42 sq.m.)22310.0North-WestUndefined1350.0NaNNaN0000110000100242
5flatapartmentsector 920.754687.01600.2Carpet area: 1600 (148.64 sq.m.)3432.0EastUndefinedNaNNaN1600.000000100001001132
6flatashiana anmolsohna road0.8811125.0791.0Super Built up area 1275(118.45 sq.m.)Carpet area: 791 sq.ft. (73.49 sq.m.)22213.0EastUndefined1275.0NaN791.000000000010001272
7flatassotech blithsector 990.926739.01365.2Super Built up area 1365(126.81 sq.m.)223+22.0NaNUnder Construction1365.0NaNNaN0000000000000562
8flatassotech blithsector 991.906702.02835.0Built Up area: 2835 (263.38 sq.m.)443+2.0North-EastUndefinedNaN2835.0NaN000000000000002
9flatats tourmalinesector 1092.308897.02585.1Super Built up area 2585(240.15 sq.m.)343+10.0EastUndefined2585.0NaNNaN0000101000010742